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Nanopore sequencing depends on the FAST5 file format, which does not allow efficient parallel analysis. Here we introduce SLOW5, an alternative format engineered for efficient parallelization and acceleration of nanopore data analysis. Using the example of DNA methylation profiling of a human genome, analysis runtime is reduced from more than two weeks to approximately 10.5 h on a typical high-performance computer. SLOW5 is approximately 25% smaller than FAST5 and delivers consistent improvements on different computer architectures. Nanopore sequencing data are rapidly analyzed with parallel data access.

Climatic changes occurring worldwide are a great threat to biodiversity and species richness of many groups of organisms thus affecting agriculture and food security. Present study deals with the butterfly populations of the coniferous forests of Murree Hills and adjacent areas of Pakistan. Inter comparisons have also been made of the current datasets with the previous archives. With a relatively short life-cycle and host-plant reliance, butterfly communities show quick response to climate change. The moist Himalayan temperate forests of Murree Hills and adjoining areas are home to many exotic species of butterflies. The weather records of the previous two decades have shown reduction in precipitation and an increase in temperature and extreme weather events. In this short span of time 14 species of butterflies have disappeared from the study area. The study shows that the remaining species are threatened due to introduction of invasive species new to this area and increased predator population. In total 46 species with Shannon index of 1.3 were recorded in the present study. One species has been recorded for the first time.

Deep Neural Networks (DNNs) rely heavily on dense arithmetic operations, motivating the use of Approximate Multipliers (AxMs) to reduce energy consumption in hardware accelerators. However, a rigorous mathematical characterization of how AxMs error distributions influence DNN accuracy remains underdeveloped. This work presents an analytical framework that connects the statistical error moments of an AxM to the induced distortion in General Matrix Multiplication (GEMM). Using the Frobenius norm of the resulting error matrix, we derive a closed form expression for practical DNN dimensions that demonstrates the distortion is predominantly governed by the multiplier mean error (bias). To evaluate this model in realistic settings, we incorporate controlled error injection into GEMM and convolution layers and examine its effect on ImageNet scale networks. The predicted distortion correlates strongly with the observed accuracy degradation, and an error configurable AxM case study implemented on an FPGA further confirms the analytical trends. By providing a lightweight alternative to behavioral or hardware level simulations, this framework enables rapid estimation of AxM impact on DNN inference quality.

Third-generation nanopore sequencers offer a feature called selective sequencing or 'Read Until' that allows genomic reads to be analyzed in real-time and abandoned halfway, if not belonging to a genomic region of 'interest'. This selective sequencing opens the door to important applications such as rapid and low-cost genetic tests. The latency in analyzing should be as low as possible for selective sequencing to be effective so that unnecessary reads can be rejected as early as possible. However, existing methods that employ subsequence Dynamic Time Warping (sDTW) algorithm for this problem are too computationally intensive that a massive workstation with dozens of CPU cores still struggles to keep up with the data rate of a mobile phone-sized MinION sequencer. In this paper, we present Hardware Accelerated Read Until (HARU), a resource-efficient hardware-software co-design-based method that exploits a low-cost and portable heterogeneous MPSoC platform with on-chip FPGA to accelerate the sDTW-based Read Until algorithm. Experimental results show that HARU on a Xilinx FPGA embedded with a 4-core ARM processor is around 2.5X faster than a highly optimized multi-threaded software version (around 85X faster than the existing unoptimized multi-threaded software) running on a sophisticated server with 36-core Intel Xeon processor for a SARS-CoV-2 dataset. The energy consumption of HARU is two orders of magnitudes lower than the same application executing on the 36-core server. Source code for HARU sDTW module is available as open-source at https://github.com/beebdev/HARU and an example application that utilises HARU is at https://github.com/beebdev/sigfish-haru.

Nanopore sequencing is an increasingly central tool for genomics. Despite rapid advances in the field, large data volumes and computational bottlenecks continue to pose major challenges. Here we introduce ex-zd, a new data compression strategy that helps address the large size of raw signal data generated during nanopore experiments. Ex-zd encompasses both a lossless compression method, which modestly outperforms all current methods for nanopore signal data compression, and a ‘lossy’ method, which can be used to achieve dramatic additional savings. The latter component works by reducing the number of bits used to encode signal data. We show that the three least significant bits in signal data generated on instruments from Oxford Nanopore Technologies (ONT) predominantly encode noise. Their removal reduces file sizes by half without impacting downstream analyses, including basecalling and detection of DNA methylation. Ex-zd compression saves hundreds of gigabytes on a single ONT sequencing experiment, thereby increasing the scalability, portability and accessibility of nanopore sequencing.

Edge training of Deep Neural Networks (DNNs) is a desirable goal for continuous learning; however, it is hindered by the enormous computational power required by training. Hardware approximate multipliers have shown their effectiveness for gaining resource-efficiency in DNN inference accelerators; however, training with approximate multipliers is largely unexplored. To build resource efficient accelerators with approximate multipliers supporting DNN training, a thorough evaluation of training convergence and accuracy for different DNN architectures and different approximate multipliers is needed. This paper presents ApproxTrain, an open-source framework that allows fast evaluation of DNN training and inference using simulated approximate multipliers. ApproxTrain is as user-friendly as TensorFlow (TF) and requires only a high-level description of a DNN architecture along with C/C++ functional models of the approximate multiplier. We improve the speed of the simulation at the multiplier level by using a novel LUT-based approximate floating-point (FP) multiplier simulator on GPU (AMSim). ApproxTrain leverages CUDA and efficiently integrates AMSim into the TensorFlow library, in order to overcome the absence of native hardware approximate multiplier in commercial GPUs. We use ApproxTrain to evaluate the convergence and accuracy of DNN training with approximate multipliers for small and large datasets (including ImageNet) using LeNets and ResNets architectures. The evaluations demonstrate similar convergence behavior and negligible change in test accuracy compared to FP32 and bfloat16 multipliers. Compared to CPU-based approximate multiplier simulations in training and inference, the GPU-accelerated ApproxTrain is more than 2500x faster. Based on highly optimized closed-source cuDNN/cuBLAS libraries with native hardware multipliers, the original TensorFlow is only 8x faster than ApproxTrain.

Nanopore sequencing is an emerging genomic technology with great potential. However, the storage and analysis of nanopore sequencing data have become major bottlenecks preventing more widespread adoption in research and clinical genomics. Here, we elucidate an inherent limitation in the file format used to store raw nanopore data – known as FAST5 – that prevents efficient analysis on high-performance computing (HPC) systems. To overcome this, we have developed SLOW5, an alternative file format that permits efficient parallelisation and, thereby, acceleration of nanopore data analysis. For example, we show that using SLOW5 format, instead of FAST5, reduces the time and cost of genome-wide DNA methylation profiling by an order of magnitude on common HPC systems, and delivers consistent improvements on a wide range of different architectures. With a simple, accessible file structure and a ~25% reduction in size compared to FAST5, SLOW5 format will deliver substantial benefits to all areas of the nanopore community.

Serotonin (5-HT) levels have been associated with several exclusively metabolic disorders. Herein, a new approach for 5-HT level as a novel biomarker of diabetes mellitus is considered using a simple nanocomposite and HPLC method. Reduced graphene oxide (rGO) comprising gold nanoparticles (AuNPs) was decorated with 18-crown-6 (18.Cr.6) to fabricate a simple nanocomposite (rGO-AuNPs-18.Cr.6). The nanocomposite was positioned on a glassy carbon electrode (GCE) to form an electrochemical sensor for the biomarker 5-HT in the presence of L-tryptophan (L-Trp), dopamine (DA), ascorbic acid (AA), urea, and glucose. The nanocomposite exhibited efficient catalytic activity for 5-HT detection by square-wave voltammetry (SWV). The proposed sensor displayed high selectivity, excellent reproducibility, notable anti-interference ability, and long-term stability even after 2 months. SWV defined a linear range of 5-HT concentration from 0.4 to 10 μg L−1. A diabetic animal model (diabetic zebrafish model) was then applied to investigate 5-HT as a novel biomarker of diabetes. A limit of detection (LOD) of about 0.33 μg L−1 was found for the diabetic group and 0.15 μg L−1 for the control group. The average levels of 5-HT obtained were 9 and 2 μg L−1 for control and diabetic groups, respectively. The recovery, relative standard deviation (RSD), and relative error (RE) were found to be about 97%, less than 2%, and around 3%, respectively. The significant reduction in 5-HT level in the diabetic group compared to the control group proved that the biomarker 5-HT can be applied for the early diagnosis of diabetes mellitus.